Analytics systems have been developed to collect and analyze large sets of data to identify trends, patterns, and correlations in the data that are not readily observable by humans due to the amount of data. In one example of analysis performed by an analytics system, a variety of additional insights are gained into operation of a service provider system within a digital medium environment, such as a web service, online provider of goods and services, and so forth. In a digital marketing scenario, for instance, this may be used to identify segments (e.g., subsets) of a user population in order to target digital marketing content to increase a likelihood of conversion. Other examples include insights into computational resource consumption by the service provider system, tracking of expenses and revenue, number of visitors to a web service, page views, and so forth.
Conventional analytics systems support a variety of techniques by which a user may interact with the data in order to gain access to this insight provided by the analytics system, such as to evaluate values of different metrics described in the analytics data via a user interface. A user, for instance, may interact with the user interface of the analytics system to view different combinations of metrics that have been associated by the system, values of those metrics, trends exhibited by the values of those metrics and correlated metrics, and so on. However, conventional analytics systems do not account for effects of individual components of digital marketing content within the analytics data. Consequently, conventional analytics systems provide a limited ability, if at all, to gain insight into how different individual components of an item of digital marketing content can affect the values of these metrics.